基于深度学习的降水降尺度方法构建及优化

牛子贤; 陈杰; 熊立华; 李爽; 柏雄风

doi:10.3799/dqkx.2025.095

Volume 50 Issue 9

Sep. 2025

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Article Navigation > Earth Science > 2025 > 50(9): 3506-3520

Niu Zixian, Chen Jie, Xiong Lihua, Li Shuang, Bai Xiongfeng, 2025. Improvement of Deep Learning Method for Daily Precipitation Downscaling. Earth Science, 50(9): 3506-3520. doi: 10.3799/dqkx.2025.095

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Niu Zixian, Chen Jie, Xiong Lihua, Li Shuang, Bai Xiongfeng, 2025. Improvement of Deep Learning Method for Daily Precipitation Downscaling. Earth Science, 50(9): 3506-3520. doi: 10.3799/dqkx.2025.095

Niu Zixian, Chen Jie, Xiong Lihua, Li Shuang, Bai Xiongfeng, 2025. Improvement of Deep Learning Method for Daily Precipitation Downscaling. Earth Science, 50(9): 3506-3520. doi: 10.3799/dqkx.2025.095

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Improvement of Deep Learning Method for Daily Precipitation Downscaling

doi: 10.3799/dqkx.2025.095

State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072

Received Date: 2025-02-17
Publish Date: 2025-09-25

Abstract

Abstract

To improve the downscaling effectiveness of deep learning methods for daily precipitation from global climate models (GCMs). Targeting at the Yangtze River basin, we constructed four deep learning downscaling models based on historical daily precipitation outputs from 20 GCMs. A hybrid method (DL-DBC) was proposed by integrating these models with the daily bias correction method. The four deep learning models exhibited comparable performance in daily precipitation downscaling. Compared to DBC, they achieved lower mean absolute relative error (MARE) for multi-year average daily precipitation but slightly higher MARE for multi-year average monthly and annual precipitation. The DL-DBC method outperformed standalone deep learning models, reducing MARE for multi-year average annual precipitation by 6.7%-11.3% and monthly precipitation by 6.3%-7.6%, while also demonstrating superior performance in precipitation frequency analysis. The DL-DBC method enhances the downscaling effectiveness of deep learning models and further reduces biases in daily precipitation data from GCMs.
- downscaling,
- deep learning,
- precipitation,
- global climate models,
- Yangtze River basin,
- climate change

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References(52)

References

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